CN110687808A - Indoor wisdom economizer system based on thing networking and machine learning - Google Patents
Indoor wisdom economizer system based on thing networking and machine learning Download PDFInfo
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Abstract
The invention discloses an indoor intelligent energy-saving system based on the Internet of things and machine learning. The invention discloses an indoor intelligent energy-saving system based on the Internet of things, which comprises a control input parameter module, an Internet of things main control unit, an Internet of things terminal node set, a terminal equipment set, an intelligent energy-saving system module and an effect feedback module. The input parameter module includes a variety of control input parameters including, but not limited to, device control events, human body sensing sensors, ambient light sensing sensors, regional information, scheduling schedules, user preferences, scene information, environmental sensors, and the like. The invention has the beneficial effects that: 1. by utilizing the matching use of the technology of the Internet of things and an intelligent algorithm, the comprehensive optimization of energy conservation, user experience and work performance can be realized; 2. and various input parameters are integrated, so that the decision is more comprehensive and scientific.
Description
Technical Field
The invention relates to the field of indoor energy conservation, in particular to an indoor intelligent energy-saving system based on the Internet of things and machine learning.
Background
The research on indoor energy saving is very many, and most of the previous researches mainly aim at improving the energy saving effect of the lighting system, and are more considered from the perspective of energy saving and consumption reduction. In fact, the indoor energy saving system should be a multi-objective system. The indoor environment is used as a main place for work, study and life of people, and the comfort level of people cannot be ignored for saving energy. Particularly, with the continuous improvement of living standard of people, the environmental requirements of people on work, study and life are higher and higher. Therefore, in such a background, the importance of improving the user experience is considered on the basis of energy saving, and energy saving control is combined with some activity scenes.
The prior art of the indoor energy-saving system is mainly divided into three categories:
the research mainly aims at improving the energy-saving effect of the lighting system:
1. the energy-efficient LED is adopted, the illumination is timely turned off, and the brightness is reduced.
2. The lighting control system inputs parameters according to a human body induction sensor, an ambient light induction sensor, regional information, a planning schedule and the like.
The research combining energy conservation and improving user experience is as follows:
1. mobile application APPs are added to the operational experience.
2. In addition to the basic adjustment of the brightness in the illumination control, the color temperature adjustment is added.
And thirdly, combining the energy conservation and the research of scene activities:
1. preliminary attempts to combine lighting control with instructional scene activity.
2. Such studies are still in the experimental and validation stages and are not applied in large-scale to real scenes.
With the continuous development of the internet of things technology, concepts of smart homes, smart classrooms and smart offices are gradually accepted by people and gradually applied to actual scenes, so that future energy conservation is not limited to a lighting system any more, but all electric appliances (such as air conditioners, computers, fans, fresh air, all-in-one machines and the like) are covered, all energy which is not wasted is saved, and waste is prevented.
The development core of the society is people oriented, so an indoor energy-saving system must be established on the basis of ensuring indoor comfort. The two are complementary and cannot be wasted.
Related art the related prior art:
an indoor energy-saving system, application number CN 201310578828.0. The invention provides an indoor energy-saving system which comprises a control host, wireless sensor equipment, a control terminal, an ammeter, a relay and controlled equipment, wherein the control host is used as a receiving end of the wireless sensor equipment to collect information; the control terminal comprises a microcomputer and a smart phone, is connected with the control host, and monitors and sends instructions to the control host; the control host is respectively connected with the ammeter and a relay switch of the controlled equipment, a control end of the relay is connected with the controlled equipment, and the control host opens and closes the relay switch according to instructions of the control terminal to achieve the purpose of controlling the controlled equipment. The invention can enable users to obtain indoor environment information at any time and any place, and can also realize the control of indoor temperature, humidity and illuminance through the control terminal according to requirements, thereby reducing the excessive consumption of energy and realizing energy conservation. The focus of this patent is to enable on-off and movement control of the device.
Indoor energy saving system, application number CN 201010148158.5. The invention discloses an indoor energy-saving system, which can be arranged on a monitoring device and comprises: the camera device is used for shooting images in an indoor monitoring area; an intelligent personnel activity state detection subsystem, coupled to the camera device, for analyzing the personnel activity state of the image in the monitored area; and a dynamic power-off subsystem coupled to the intelligent personnel activity status detection subsystem for handling the on/off of the indoor power. The indoor energy-saving system of the embodiment of the invention combines the background identification and the object tracking technology, tracks the personnel condition through the camera, monitors the personnel activity in the area, and can immediately and effectively react to the personnel activity condition and turn on/off the power supply so as to achieve the energy-saving effect. The key point of the patent is to detect whether a person is present through monitoring to realize on-off control of the light.
An indoor energy-saving system, application number CN 201621404185.3. The invention relates to an indoor energy-saving system, which comprises an air conditioner, a door frame, a door body and an electric door closing device, wherein the door body is connected with the door frame through a hinge; the photoelectric sensors are arranged on the door frame and arranged in a front-back mode; the counter is connected with the photoelectric sensor; and the controller is connected with the air conditioner and used for controlling the on-off of the air conditioner, and the controller is also connected with the counter. The invention can automatically close the classroom door when the indoor air conditioner runs, thereby improving the refrigerating or heating efficiency of the indoor air conditioner. The focus of this patent is to save energy by air conditioning.
The application of LED intelligent lighting in classrooms of primary and middle schools is from lighting engineering newspaper, volume 27 and the third period of 2016, 6 months. The document designs an LED intelligent lighting system suitable for classroom lighting of primary and secondary schools. The system mainly focuses on improving the overall level of classroom illumination, and the starting point is the comfort level of classroom illumination.
The intelligent control of the classroom lighting system comes from the natural science journal of the university of Heilongjiang, Vol 23, No 3, 6/2006. From the viewpoint of energy saving, the document uses a far infrared device, an illuminometer (photoelectric tube) and an auxiliary circuit to form an intelligent control device of a classroom lighting system. The device can realize that the classroom is automatic when nobody or illumination is sufficient and closes the lamp, and someone comes and automatic the turning on lamp when illumination is not enough, comes to light the light that corresponds the region according to the difference of how many and the position of people in the classroom. The energy-saving purpose is achieved, and the utilization rate of the campus electric energy is obviously improved. The focus of this document is classroom lighting energy conservation.
A Smart Lighting Control to Save Energy from The 6th IEEEEEInternational Conference on Intelligent Data Acquisition and advanced computing Systems, Technology and Applications, 15-17September 2011, Prague, Czech Republic. The document combines parameters such as a human body induction sensor, an ambient light induction sensor, regional information, a planning schedule and the like to realize energy-saving management.
The traditional technology has the following technical problems:
1. the technology of energy saving is realized by only replacing the high-efficiency LED, turning off the illumination timely and reducing the brightness timely, and the energy saving is relatively single and has no intellectualization;
2. most of technical research footholds are energy conservation of indoor lighting systems or air conditioning systems, but energy conservation of other electrical equipment (such as computers) is not achieved;
3. the energy-saving algorithm is simple, whether equipment is switched on or off is basically judged through a small quantity of parameters and preset rules, and the requirements of various equipment, multiple environmental parameters, high personalized requirements and the like cannot be met;
4. some techniques considering user experience mainly improve operation experience through remote control of some mobile APPs, and such attempts are also more superficial, and more still stay at the level of manual control;
5. the difference of the requirements of the user using scenes on the environmental parameters (such as brightness, color temperature, temperature and humidity) is not considered, and the exploration on the user experience is not deep enough.
Disclosure of Invention
The invention aims to solve the technical problem of providing an indoor intelligent energy-saving system based on the Internet of things and machine learning, and discloses an indoor intelligent energy-saving system based on the Internet of things and machine learning. The system simultaneously considers multiple targets of energy conservation, scene application, manageability, user experience and the like. Sensory sensors, regional information, scheduling schedules, user preferences, situational information, environmental sensors (e.g., temperature, humidity, PM2.5, PM10, formaldehyde, TVOC, carbon dioxide, etc.), and the like. The invention discloses a system, which is an intelligent energy-saving system capable of continuously evolving and realized by combining various decision engines such as a rule engine, a fuzzy engine and machine learning. The experience and the comfort level of the user are fully considered, and the device state is different from one person to another instead of being experienced in a uniform state by designing various scene configurations. The method can automatically learn and optimize various parameter settings according to the big data to achieve the optimal configuration. Energy conservation and user experience are unified, and energy conservation is not pursued one-sidedly.
In order to solve the technical problem, the invention provides an indoor intelligent energy-saving system based on the internet of things and machine learning, which comprises: the intelligent energy-saving system comprises a control input parameter module, an Internet of things main control unit, an Internet of things terminal node set, a terminal equipment set and an intelligent energy-saving system module;
the input parameter module comprises a plurality of control input parameters, and the plurality of control input parameters comprise equipment control events, human body induction sensors, ambient light induction sensors, area information, plan schedules, user preferences, scene information and environmental sensors;
the Internet of things main control unit is responsible for communication between the intelligent energy-saving system and the Internet of things terminal node;
the Internet of things terminal node assembly realizes communication between the electric equipment and the Internet of things main control unit by an Internet of things module configured on each electric equipment;
the terminal equipment is integrated into various electrical equipment, including adjustable light and color lamps, air conditioners, fresh air, fans, humidifiers, electric curtains, televisions and projectors;
the intelligent energy-saving system module consists of a front-end configuration client module, a background service program module and a scene database module; the front-end configuration client module configures an energy-saving scheme and checks the execution condition of the energy-saving scheme, wherein the configuration of the energy-saving scheme refers to the combination of setting an algorithm and control parameters according to a scene; the scene database module is used for recording configuration data of each scene of the scene; the three algorithm engines of the background service program module are responsible for executing an energy-saving scheme, namely, control is carried out according to input parameters and by combining scene configuration data, wherein the three algorithm engines are a rule engine, a Fuzzy engine and an AI engine.
In one embodiment, the main control unit of the internet of things supports mainstream communication protocols including at least one of Wi-Fi, RF2.4, 433M, Zigbee.
In one embodiment, the rule engine sets the control parameters using the precise values of the parameters, and the control parameters are specifically as follows:
in one embodiment, the Fuzzy engine sets control parameters using Fuzzy theory, and the control parameters are specifically as follows:
| serial number | Controlling input parameters | Parameter range |
| 1 | Intelligent light sensor | Very bright, very dark, slightly bright, slightly dark, etc. illumination |
| 2 | Planning a schedule | Working day, holiday |
| 3 | Environmental sensor | Very hot temperature, very high carbon dioxide concentration, etc |
And the Fuzzy engine checks the characteristic value of each environmental parameter according to the relevant national standard and configures the parameters by adopting a Fuzzy comprehensive evaluation method.
In one embodiment, the AI engine employs a machine learning algorithm, and includes the following specific steps:
step 1: the input data samples, i.e., input parameters, include body sensors, smart light sensor, region information, planning schedules, user preferences, scene information, and environmental sensors
Step 2: entering of input parameters into the intelligent object triggers an Action (A)t) Then the State of the Environment (S)t) It will change. At this time, a delay Reward (R) is outputt)；
Repeating the steps 1 and 2 to generate new rewards; the delay reward function is
Rt＝WeE+WpP+WwW
Wherein E is an energy-saving variable, P is a user preference variable, and W is a work performance variable; weFor energy-saving weighting, WpFor user preference weight, WwA work performance weight; the determination of the weight of the variable E, P, W can be dynamically set according to actual scene requirements, and the assignment of the weight is autonomously determined by the user according to the specific use condition of the place in other use scenes. The machine learning algorithm allows the user to adjust the ratio of each item of weight according to the change of the business objective, thereby modifying the reward function and realizing the self-adaption aiming at the new objective combination.
In one embodiment, the student performance and energy conservation are emphasized under the school scene, and the weight ratio of the two items can be set to be a little higher (W)w＝50％，We＝30％，Wp＝20％)。
In one embodiment, the shopping mall scene is weighted more heavily on performance (sales) and shopping experience (preferences) (Ww 50%, Wp 40%, and We 10%).
In one embodiment, the system further comprises an effect feedback module, wherein the effect feedback module comprises energy-saving effect statistical data, user experience data and work performance data.
In one embodiment, the sustainable evolution function of the intelligent energy-saving system is embodied as that the output of the intelligent energy-saving system can bring about improvement of energy-saving effect, change of user experience and change of work performance, and the output can continuously optimize the scene configuration database, so that the configuration of the scene is continuously optimized, and the sustainable evolution of the system is realized;
the energy-saving effect acquisition and measurement mode is as follows: the operation time length of each equipment is multiplied by the power of each equipment to be comprehensively calculated;
the user experience acquisition and measurement mode is as follows: triggering according to the automatic action within a certain time; the number of active user interventions and the amplitude of adjustment;
the method for acquiring and measuring the work performance comprises the following steps: and automatically capturing performance data by adopting a data interface, wherein the performance data comprises at least one of examination scores, class ranking data, sales performance and passenger flow of students.
In one embodiment, the effect feedback value vectors are normalized separately.
The invention has the beneficial effects that:
1. by utilizing the matching use of the technology of the Internet of things and an intelligent algorithm, the comprehensive optimization of energy conservation, user experience and work performance can be realized;
2. various input parameters are integrated, so that the decision is more comprehensive and scientific;
3. the advantages are complementary and the algorithm is more effective by using a rule engine, a fuzzy engine and an AI engine in an overview;
the AI engine has the autonomous learning capability and supports the dynamic adjustment of a plurality of target weights;
5. the system can be used for controlling various electrical equipment and has universal applicability;
6. the combination with the scene can avoid false triggering caused by human body failure detection in a static state of the traditional pyroelectric human body sensor, and extra hardware cost is not required to be added.
7. The intelligent control enables the equipment to be started as required, so that the working time of the equipment is effectively reduced, and the service life of the equipment is prolonged.
8. The design which uniformly considers energy conservation and user comfort better meets the requirement of future development.
Drawings
Fig. 1 is a schematic structural diagram of an indoor intelligent energy-saving system based on internet of things and machine learning according to the present invention.
Fig. 2 is a schematic diagram of a background service program module in the indoor intelligent energy-saving system based on internet of things and machine learning according to the present invention.
Fig. 3 is a flowchart of a machine learning algorithm adopted in the indoor intelligent energy-saving system based on the internet of things and machine learning according to the present invention.
Fig. 4 is a schematic diagram of an effect feedback module in the indoor intelligent energy-saving system based on internet of things and machine learning according to the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Referring to fig. 1, the invention discloses an indoor intelligent energy-saving system based on the internet of things, which comprises a control input parameter module, an internet of things main control unit, an internet of things terminal node set, a terminal equipment set, an intelligent energy-saving system module and an effect feedback module. The input parameter module comprises a plurality of control input parameters, wherein the plurality of control input parameters comprise but are not limited to equipment control events, human body induction sensors, ambient light induction sensors, regional information, planning schedules, user preferences, scene information, environmental sensors and the like; the Internet of things main control unit is responsible for realizing communication between the intelligent energy-saving system and the Internet of things terminal node; the Internet of things terminal node assembly realizes communication between the electric equipment and the Internet of things main control unit by an Internet of things module configured on each electric equipment; the terminal equipment is integrated into various electrical equipment, including but not limited to adjustable light and color light, air conditioner, new trend, fan, humidifier, electric curtain, TV, projector, etc. The intelligent energy-saving system module consists of a front-end configuration client module, a background service program module and a scene database module; and the effect feedback module comprises but not limited to energy-saving effect statistical data, user experience data, work performance data and the like.
Firstly, the main control modes for controlling the input parameters are classified as follows:
the above 8 kinds of control input parameters can be used as single input of the intelligent energy-saving system, and can also be used as input by combining multiple kinds at will.
Second, thing networking main control unit module
The Internet of things main control unit is responsible for communication between the intelligent energy-saving system and the Internet of things terminal nodes. The main control unit of the internet of things supports mainstream communication protocols such as Wi-Fi, RF2.4, 433M, Zigbee and the like.
Third, thing networking terminal node integrated module
The Internet of things terminal nodes are integrated into Internet of things modules configured on the electrical equipment, and communication between the electrical equipment and the Internet of things main control unit is realized. The terminal nodes of the Internet of things support mainstream communication protocols such as Wi-Fi, RF2.4, 433M, Zigbee and the like.
Fourth, terminal equipment assembly module
The terminal equipment is integrated into various controlled electric equipment. Such as lighting fixtures, air conditioners, curtains, fresh air, fans, etc.
Fifthly, the intelligent energy-saving system module mainly comprises three modules:
a first module: the front-end configures a client module, which configures the energy-saving scheme (according to the combination of the scene setting algorithm and the control parameters) and checks the execution condition of the energy-saving scheme.
And a second module: and the scene database module is used for recording the configuration data of each scene of the scene.
And a third module: and the background service program module is responsible for executing an energy-saving scheme by three algorithm engines (a rule engine, a Fuzzy engine and an AI engine), namely controlling according to input parameters and combining scene configuration data.
A rule engine for setting control parameters by using the precise values of the parameters
The rule engine adjusts and optimizes the parameters by adopting the accurate range values of the parameters, and the rule engine is suitable for the users who have high requirements on the accuracy of the control parameters and have relatively accurate knowledge on the numerical range of the parameters.
Fuzzy engine, using Fuzzy theory to set control parameters
| Serial number | Controlling input parameters | Parameter range |
| 1 | Intelligent light sensor | Very bright, very dark, slightly bright, slightly dark, etc. illumination |
| 2 | Planning a schedule | Working day, holiday |
| 3 | Environmental sensor | Very hot temperature, very high carbon dioxide concentration, etc |
The Fuzzy engine checks the characteristic values (such as brightness level, carbon dioxide concentration level, comfort level and the like) of each environmental parameter according to the relevant national standard, and then configures the parameters by adopting a Fuzzy comprehensive evaluation method, so that the Fuzzy comprehensive evaluation method is suitable for users who have no accurate mathematical concept on some parameters and only have visual feeling, and is simple in setting.
AI engine, using machine learning algorithm
Step 1: the input data samples, i.e., input parameters, include human body sensors, smart light sensing sensors, regional information, planning schedules, user preferences, scene information, environmental sensors, and the like.
Step 2: entering an agent (algorithm) by entering parameters triggers an Action (A)t) Then the State of the Environment (S)t) Changes are made (which in turn facilitates optimization of the input parameters). At this time, a delay Reward (R) is outputt). Repeating steps 1 and 2 will generate a new prize. The delay reward function is
Rt＝WeE+WpP+WwW
Wherein E is an energy-saving variable, P is a user preference variable, and W is a work performance variable; weFor energy-saving weighting, WpFor user preference weight, WwIs the work performance weight.
The weight of the variable E, P, W can be dynamically set according to actual scene requirements, such as student performance and energy conservation under school scene, the weight ratio of the two items can be set to be a little higher (W)w＝50％，We＝30％， Wp20%); the two weights can be adjusted to be high (W) by focusing on the performance (sales) and the shopping experience (preference) under the market scenew＝50％，Wp＝40％，We10%), other usage scenarios have the assignment of weights determined autonomously by the user, depending on the specific use of the venue. It should be noted that the foregoing algorithm allows the user to adjust the ratio of each weight according to the change of the business objective, so as to modify the reward function, and implement the adaptation to the new objective combination.
Effect feedback module
The sustainable evolution function of the intelligent energy-saving system is embodied as that the output of the intelligent energy-saving system can bring about improvement of energy-saving effect, change of user experience and change of work performance, and the output can continuously optimize the scene configuration database, so that the configuration of the scene is continuously optimized, and the sustainable evolution of the system is realized.
The energy-saving effect acquisition and measurement mode is as follows: the operating duration of each device is multiplied by the power of the respective device for integrated calculation.
The user experience acquisition and measurement mode is as follows: the number of active user interventions and the magnitude of the adjustments are measured in terms of the time (a threshold is set, e.g., 10 minutes) after the automatic action is triggered.
The method for acquiring and measuring the work performance comprises the following steps: and a data interface is adopted to automatically capture performance data, such as examination scores, class ranking data, sales performance, passenger flow and the like of students.
The effect feedback value vector is composed of the three effect feedback data, and since the three data in the vector are not data of the same dimension, in order to eliminate dimension influence among indexes, normalization processing needs to be performed on the effect feedback value vector respectively.
A specific application scenario of the present invention is described below:
taking classroom as an example of application scenarios:
the classroom is equipped with the following internet of things devices: the intelligent energy-saving system comprises a lighting lamp, an air conditioner, fresh air, an electric curtain, a fan, an electronic blackboard, a human body sensor, an environment light sensor and an environment sensor (a temperature sensor, a humidity sensor, a PM2.5 sensor, a PM10 sensor, a formaldehyde sensor, a TVOC sensor and a carbon dioxide sensor), wherein the human body sensor, the environment light sensor and the environment sensor are used as control input parameters of the intelligent energy-saving system; electrical equipment such as lighting lamps, air conditioners, fresh air, electric curtains, fans, electronic blackboards and the like are used as controlled equipment of the intelligent energy-saving system. The intelligent energy-saving system is deployed on a third-party cloud server (or an IDC machine room of a school).
School logistics personnel can log in the intelligent energy-saving system through a PC computer to configure an energy-saving scheme client, and set an energy-saving scheme policy as follows (meanwhile, the policy can be applied to a plurality of schools or classroom areas):
the weights of three variables (E: energy saving variable, P: user preference variable, W: work performance variable) of the AI engine reward function are set, Ww is 50%, We is 30%, and Wp is 20%.
Before class: human body induction and temperature sensor are combined. Keeping the light off, turning on the air conditioner 15 minutes in advance according to the ambient temperature, and turning on the light if people are detected to arrive at the classroom.
In class: human body sensor, environment light perception sensor, environment sensor and teaching scene's combination detects someone through human body sensor, and environment light perception sensor detects indoor illuminance and is bright on the right side, and wisdom economizer system can reduce the illuminance automatically, and environment light perception sensor detects indoor illuminance and is dark on the right side, and wisdom economizer system can be automatic with the illuminance increasement, and the prerequisite of reducing and increaseing is the illuminance that still guarantees national standard regulation's scene of giving lessons requirement. And the on-off and running states of the air conditioner, the fresh air and the fan are dynamically controlled according to environmental parameters such as temperature, PM2.5, carbon dioxide concentration and the like.
Rest in the break: human body induction, ambient light sensation and temperature sensors are combined, the electronic blackboard, fresh air and other equipment are closed, and if no one exists, the light, air conditioner and other equipment are also closed; if a person is detected, the light is adjusted to relatively low brightness according to the ambient light, and the air conditioner temperature is slightly higher (summer)/lower (winter) than the normal setting by 2 degrees according to the ambient temperature, so that the energy-saving purpose is achieved.
After school is placed: turning off all the devices, starting human body induction and ambient light induction, and turning on a lamp and adjusting the light to relatively low brightness if people are detected and the ambient light is dark; if no person is detected for 15 seconds, the light is turned off.
Human body sensor, temperature sensor and teaching scene's combination, for example under the scene of meeting room, detect someone through human body sensor and be in the meeting, temperature sensor detects indoor temperature on the high side, and wisdom economizer system can be automatic with the air conditioner temperature reduction and after the meeting, can the self-closing air conditioner when human body sensor detects nobody.
A combination of user preferences and instructional scenarios. After someone has controlled equipment state through wall switch or intelligent terminal, user's operation can cover wisdom economizer system automatically, for example the light of classroom lamp is shaded, and after the manual accent of student, wisdom economizer system can filter the light luminance under the current scene automatically, resumes until next classroom scene again. At the same time, the user experience (WpP) in the machine learning algorithm reward function is enhanced. In other words, if similar user operations occur multiple times in the teaching scenario, the algorithm model is more inclined to automatically adopt similar operations when detecting approximate input parameters after being repeatedly strengthened.
Human sensor, environment light perception sensor and teaching scene's combination, for example when summer sunshine is stronger, the peer of leaning on the window receives the influence of sunshine, detects someone through human sensor, and when environment light perception sensor detected indoor illuminance very strong, wisdom economizer system can self-closing (window) curtain.
Various combinations of input parameters and device controls are possible, and these are merely examples of preferred embodiments and are not all described in one row.
The background service program of the intelligent energy-saving system can dynamically execute the strategy of the energy-saving scheme to realize energy saving. With the increase of data volume and the accumulation of long-term use of the system, the system can autonomously learn an optimization model, so that more optimal configuration of energy conservation is achieved, and the system is more efficient and comfortable.
The above embodiments are only typical embodiments of the patent, and are also applicable to indoor energy saving in other industries (such as hotels, offices, shopping malls, etc.).
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. The utility model provides an indoor wisdom economizer system based on thing networking and machine learning which characterized in that includes: the intelligent energy-saving system comprises a control input parameter module, an Internet of things main control unit, an Internet of things terminal node set, a terminal equipment set and an intelligent energy-saving system module;
the input parameter module comprises a plurality of control input parameters, and the plurality of control input parameters comprise equipment control events, human body induction sensors, ambient light induction sensors, area information, plan schedules, user preferences, scene information and environmental sensors;
the Internet of things main control unit is responsible for communication between the intelligent energy-saving system and the Internet of things terminal node;
the Internet of things terminal node assembly realizes communication between the electric equipment and the Internet of things main control unit by an Internet of things module configured on each electric equipment;
the terminal equipment is integrated into various electrical equipment, including adjustable light and color lamps, air conditioners, fresh air, fans, humidifiers, electric curtains, televisions and projectors;
the intelligent energy-saving system module consists of a front-end configuration client module, a background service program module and a scene database module; the front-end configuration client module configures an energy-saving scheme and checks the execution condition of the energy-saving scheme, wherein the configuration of the energy-saving scheme refers to the combination of setting an algorithm and control parameters according to a scene; the scene database module is used for recording configuration data of each scene of the scene; the three algorithm engines of the background service program module are responsible for executing an energy-saving scheme, namely, control is carried out according to input parameters and by combining scene configuration data, wherein the three algorithm engines are a rule engine, a Fuzzy engine and an AI engine.
2. The internet of things and machine learning based indoor intelligent energy saving system of claim 1, wherein the internet of things main control unit supports mainstream communication protocols including at least one of Wi-Fi, RF2.4, 433M, Zigbee.
3. The internet of things and machine learning based indoor intelligent energy-saving system as claimed in claim 1, wherein the rule engine adopts the precise values of the parameters to set the control parameters, and the control parameters are as follows:
4. the internet of things and machine learning-based indoor intelligent energy-saving system of claim 1, wherein the Fuzzy engine sets control parameters by using Fuzzy theory, and the control parameters are specifically as follows:
And the Fuzzy engine checks the characteristic value of each environmental parameter according to the relevant national standard and configures the parameters by adopting a Fuzzy comprehensive evaluation method.
5. The internet of things and machine learning based indoor intelligent energy-saving system of claim 1, wherein the AI engine adopts a machine learning algorithm, and the specific steps are as follows:
step 1: the input data samples, i.e., input parameters, include body sensors, smart light sensor, region information, planning schedules, user preferences, scene information, and environmental sensors
Step 2: entering of input parameters into the intelligent object triggers an Action (A)t) Then the State of the Environment (S)t) It will change. At this time, a delay Reward (R) is outputt)；
Repeating the steps 1 and 2 to generate new rewards; the delay reward function is
Rt＝WeE+WpP+WwW
Wherein E is an energy-saving variable, P is a user preference variable, and W is a work performance variable; weFor energy-saving weighting, WpFor user preference weight, WwA work performance weight; the determination of the weight of the variable E, P, W can be dynamically set according to actual scene requirements, and the assignment of the weight is autonomously determined by the user according to the specific use condition of the place in other use scenes. The machine learning algorithm allows the user to adjust the ratio of each item of weight according to the change of the business objective, thereby modifying the reward function and realizing the self-adaption aiming at the new objective combination.
6. The internet of things and machine learning-based indoor intelligent energy-saving system of claim 5, wherein the weight ratio of the student performance and the energy conservation under the school scene can be set a little higher (W)w＝50％，We＝30％，Wp＝20％)。
7. The internet-of-things and machine learning based indoor intelligent energy saving system as claimed in claim 5, wherein the ratio of the two weights can be increased (Ww 50%, Wp 40%, and We 10%) for the performance (sales) and shopping experience (preference) in the market scene.
8. The internet of things and machine learning based indoor intelligent energy saving system of claim 1, further comprising an effect feedback module, wherein the effect feedback module comprises energy saving effect statistics data, user experience data and work performance data.
9. The internet of things and machine learning based indoor intelligent energy-saving system as claimed in claim 1, wherein the sustainable evolution function of the intelligent energy-saving system is embodied as improvement of energy-saving effect, change of user experience and change of work performance through output of the intelligent energy-saving system, which in turn continuously optimizes the scene configuration database, so that the configuration of the scene is continuously optimized, thereby realizing sustainable evolution of the system;
the energy-saving effect acquisition and measurement mode is as follows: the operation time length of each equipment is multiplied by the power of each equipment to be comprehensively calculated;
the user experience acquisition and measurement mode is as follows: triggering according to the automatic action within a certain time; the number of active user interventions and the amplitude of adjustment;
the method for acquiring and measuring the work performance comprises the following steps: and automatically capturing performance data by adopting a data interface, wherein the performance data comprises at least one of examination scores, class ranking data, sales performance and passenger flow of students.
10. The internet of things and machine learning based indoor intelligent energy-saving system as claimed in claim 9, wherein the effect feedback value vectors are normalized respectively.
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| CN201910973762.2A CN110687808A (en) | 2019-10-14 | 2019-10-14 | Indoor wisdom economizer system based on thing networking and machine learning |
| PCT/CN2019/124532 WO2021072951A1 (en) | 2019-10-14 | 2019-12-11 | Internet of things and machine learning-based smart indoor energy conservation system |
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